Improving finger vein discriminant representation using dynamic margin softmax loss

With the increasing demand for secure biometric identification systems, finger vein recognition has received widespread attention. Recent studies have made progress in the verification of finger veins, but the extraction of the discriminative features of finger veins from images remains challenging. Although the traditional method of extracting features by combining softmax function and cross-entropy loss function can achieve separation between classes, it lacks discriminability. In addition, the method of setting a fixed margin yields good results, but it may cause some features to overlap. We argue that when some features are separated well from other features, the margin set needs to be reduced. Therefore, a dynamic margin softmax loss (dynamic softmax) is proposed in this study to obtain discriminative image features. Features and weight vectors are normalized, and the loss function dynamics are subsequently adjusted to achieve different cosine intervals for different classes. The main idea of this method is to maximize the distance between inter-class and minimize the distance between intra-class. This method can keep features separated without increasing the complexity of optimizing the neural network model. It is simpler and more effective than other loss functions. Experiments prove the effectiveness of the proposed method for finger vein recognition.